Upload files to 'Scenario2/new'

Scenario2/new/new_model.py

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+from model import DeepTraCDR, ModelOptimizer
+from data_sampler import NewSampler
+import torch
+def run_DeepTraCDR(cell_exprs, drug_finger, res_mat, null_mask, target_dim, target_index,
+               evaluate_fun, args):
+
+        # Convert to numpy if needed
+    if isinstance(res_mat, torch.Tensor):
+        res_mat = res_mat.numpy()
+    if isinstance(null_mask, torch.Tensor):
+        null_mask = null_mask.numpy()
+    # Initialize sampler with required parameters
+    sampler = NewSampler(res_mat, null_mask, target_dim, target_index)
+    
+    # Initialize model with correct device parameter name
+    model = DeepTraCDR(
+        adj_mat=sampler.train_data,
+        cell_exprs=cell_exprs,
+        drug_fingerprints=drug_finger,
+        layer_size=args.layer_size,
+        gamma=args.gamma,
+        device=args.device  
+    )
+    
+    # Initialize optimizer with correct parameter order
+    opt = ModelOptimizer(
+        model=model,
+        train_data=sampler.train_data,
+        test_data=sampler.test_data,
+        test_mask=sampler.test_mask,
+        train_mask=sampler.train_mask,
+        adj_matrix=res_mat,  
+        evaluate_fun=evaluate_fun,
+        lr=args.lr,
+        wd=args.wd,
+        epochs=args.epochs,
+        test_freq=args.test_freq if hasattr(args, 'test_freq') else 20,
+        device=args.device
+    )
+    
+    # Call train method and unpack results
+    results = opt.train()
+    
+    return results[0], results[1] # true_data, predict_data, auc_data